@article{31672,
  keywords = {Prediction, Deep learning, Occupant count, Predictive control, Plug loads, Long short-term memory networks},
  author = {Zhe Wang and Tianzhen Hong and Mary Ann Piette},
  title = {Predicting plug loads with occupant count data through a deep learning approach},
  abstract = {<p>Predictive control has gained increasing attention for its ability to reduce energy consumption and improve occupant comfort in buildings. The plug loads prediction is a key component for the predictive building controls, as plug loads is a major source of internal heat gains in buildings. This study proposed a novel method to apply the Long-Short-Term-Memory (LSTM) Network, a special form of Recurrent Neural Network, to predict plug loads. The occupant count and the time have been confirmed to drive the plug load profile and thus selected as the features for the plug load prediction. The LSTM network was trained and tested with ground truth occupant count data collected from a real office building in Berkeley, California. Results from the LSTM network markedly improve the prediction accuracy compared with traditional linear regression methods and the classical Artificial Neural Network. 95% of 1-h predictions from LSTM network are within ±1 kW of the actual plug loads, given the average plug loads during the office hour is 8.6 kW. The CV(RMSE) of the predicted plug load is 11% for the next hour, and 20% for the next 8 h. Lastly, we compared four prediction approaches with the office building we monitored: LSTM vs. ARIMA, with occupant counts vs. without occupant counts. It was found, the prediction error of the LSTM approach is around 4% less than the ARIMA approach. Using occupant counts as an exogenous input could further reduce the prediction error by 5%–6%. The findings of this paper could shed light on the plug load prediction for building control optimizations such as model-predictive control.</p>},
  year = {2019},
  journal = {Energy},
  volume = {181},
  pages = {29 - 42},
  month = {05/2019},
  issn = {03605442},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0360544219310205},
  doi = {10.1016/j.energy.2019.05.138},
  language = {eng},
}